Utilization of an artificial intelligence–enhanced, web‐based application to review bile duct brushing cytologic specimens: A pilot study

Author:

Marya Neil B.12ORCID,Powers Patrick D.1,Bois Melanie C.3,Hartley Christopher3,Kerr Sarah E.4ORCID,Thangaiah Judith Jebastin3ORCID,Norton Daniel5,Abu Dayyeh Barham K.6,Cantley Richard7ORCID,Chandrasekhara Vinay6,Gores Gregory6,Gleeson Ferga C.6,Law Ryan J.6,Maleki Zahra8ORCID,Martin John A.6,Pantanowitz Liron9ORCID,Petersen Bret6,Storm Andrew C.6,Levy Michael J.6,Graham Rondell P.3

Affiliation:

1. Program in Digital Medicine University of Massachusetts Chan Medical School Worcester Massachusetts USA

2. Division of Gastroenterology University of Massachusetts Chan Medical School Worcester Massachusetts USA

3. Department of Laboratory Medicine and Pathology Mayo Clinic Rochester Minnesota USA

4. Allina Health Laboratory Minneapolis Minnesota USA

5. Independent researcher Boston Massachusetts USA

6. Division of Gastroenterology and Hepatology Mayo Clinic Rochester Minnesota USA

7. Department of Pathology University of Michigan Ann Arbor Michigan USA

8. Department of Pathology Johns Hopkins Hospital Baltimore Maryland USA

9. Department of Pathology University of Pittsburgh School of Medicine Pittsburgh Pennsylvania USA

Abstract

AbstractBackgroundThe authors previously developed an artificial intelligence (AI) to assist cytologists in the evaluation of digital whole‐slide images (WSIs) generated from bile duct brushing specimens. The aim of this trial was to assess the efficiency and accuracy of cytologists using a novel application with this AI tool.MethodsConsecutive bile duct brushing WSIs from indeterminate strictures were obtained. A multidisciplinary panel reviewed all relevant information and provided a central interpretation for each WSI as being “positive,” “negative,” or “indeterminate.” The WSIs were then uploaded to the AI application. The AI scored each WSI as positive or negative for malignancy (i.e., computer‐aided diagnosis [CADx]). For each WSI, the AI prioritized cytologic tiles by the likelihood that malignant material was present in the tile. Via the AI, blinded cytologists reviewed all WSIs and provided interpretations (i.e., computer‐aided detection [CADe]). The diagnostic accuracies of the WSI evaluation via CADx, CADe, and the original clinical cytologic interpretation (official cytologic interpretation [OCI]) were compared.ResultsOf the 84 WSIs, 15 were positive, 42 were negative, and 27 were indeterminate after central review. The WSIs generated on average 141,950 tiles each. Cytologists using the AI evaluated 10.5 tiles per WSI before making an interpretation. Additionally, cytologists required an average of 84.1 s of total WSI evaluation. WSI interpretation accuracies for CADx (0.754; 95% CI, 0.622–0.859), CADe (0.807; 95% CI, 0.750–0.856), and OCI (0.807; 95% CI, 0.671–0.900) were similar.ConclusionsThis trial demonstrates that an AI application allows cytologists to perform a triaged review of WSIs while maintaining accuracy.

Publisher

Wiley

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